Trustworthiness Tendency Incremental Extraction Using Information Gain

Computational trust systems are getting popular in several domains such as social networks, grid computing and business-to-business systems. However, the estimation of the trustworthiness of agents is not trivial in scenarios where the existing trust evidences are scarce. We propose an online, situation-aware trust model that uses the information gain metric to dynamically extract tendencies of failure of target agents, improving the process of selection of partners in a relevant way. Experimental results presented in this paper show that our proposal outperforms other trust approaches in contextual scenarios.